Emergent Behaviors in a Resilient Logistics Supply Chain

Research output: ThesisPhD Thesis - Research UT, graduation UT

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Abstract

This PhD dissertation addresses vulnerabilities in logistics supply chains, such as disruptions from pandemics, natural disasters, and geopolitical tensions. It underscores the complexity of supply chains, likening them to socio-technical systems where resilience is key for managing unexpected events and thriving amidst adversity. The focus is on leveraging smart business objects—exemplified by “smart pallets” with sensing and computational capabilities—to augment real-time decision-making and resilience in supply chains. When strategically positioned within the supply network, these smart pallets can provide key insights into the movement of goods, enabling a rapid response to disruptions through real-time monitoring and predictive analytics. The dissertation investigates centralized, decentralized, and hybrid approaches to decision-making within these networks. Centralized methods ensure uniformity but may neglect local specifics, while decentralized ones offer adaptability at the risk of inconsistency. A hybrid model seeks to balance these extremes, combining broad guidelines with local autonomy for optimal resilience. This research aims to explore how such smart objects can anticipate and react to emergent behaviors, thereby augmenting supply chain resilience beyond mere performance indicators to actively managing and adapting to disruptions. Through various chapters, the dissertation offers an exploration, from designing resilient architectures and evaluating business rules in real-time to mining these rules from data and adapting them to evolving circumstances. Overall, this work presents a nuanced view of resilience in supply chains, emphasizing the adaptability of business rules, the importance of technological evolution alongside organizational practices, and the potential of integrating novel techniques such as process mining with multi-agent systems for better decision-making and operational efficiency.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Havinga, Paul J.M., Supervisor
  • Iacob, Maria Eugenia, Supervisor
Award date3 May 2024
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-6072-6
Electronic ISBNs978-90-365-6073-3
DOIs
Publication statusPublished - 17 Apr 2024

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